feat(chart): real-world developer AI adoption and code quality
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Orchestrator
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src/charts/developer_reality.py
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222
src/charts/developer_reality.py
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"""Real-World Developer AI Adoption and Code Quality Chart"""
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent.parent))
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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from src.data.agent_adoption import (
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developer_ai_adoption, code_acceptance_rates,
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code_quality_in_production, failure_modes
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)
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from src.utils.styling import (
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get_theme, EXPORT_DPI, AGENT_GROWTH, BUBBLE_ZONE,
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WARNING_ZONE, NORMAL_ZONE, GRAY_DARK, GRAY_LIGHT
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)
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def plot_developer_reality() -> str:
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plt.rcParams.update(get_theme())
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fig = plt.figure(figsize=(16, 12))
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fig.set_facecolor("#ffffff")
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# --- Layout: 3 panels via GridSpec ---
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from matplotlib.gridspec import GridSpec
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gs = GridSpec(2, 2, figure=fig,
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height_ratios=[1, 1.5],
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hspace=0.45, wspace=0.30,
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left=0.07, right=0.93, top=0.88, bottom=0.06)
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ax1 = fig.add_subplot(gs[0, 0]) # Panel A — Adoption
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ax2 = fig.add_subplot(gs[0, 1]) # Panel B — Acceptance
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ax3 = fig.add_subplot(gs[1, :]) # Panel C — Code Quality (full width)
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# =========================================================
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# Panel A: AI Coding Tool Adoption Rates
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# =========================================================
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adoption_labels = [
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"84% use or plan AI tools\n(Stack Overflow 2025)",
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"51% professional devs\nuse AI daily (Stack Overflow)",
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"85% regular AI usage\n(JetBrains 2025)",
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"62% rely on coding\nassistant (JetBrains)",
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"91% AI adoption in active\nrepos (DX DevCycle)",
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]
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adoption_values = [84, 51, 85, 62, 91]
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adoption_colors = [
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AGENT_GROWTH, AGENT_GROWTH,
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"#5b2d8e", "#3c1d6e",
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AGENT_GROWTH,
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]
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bars_a = ax1.barh(
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range(len(adoption_labels)),
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adoption_values,
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color=adoption_colors,
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edgecolor="white",
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height=0.55,
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)
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ax1.set_yticks(range(len(adoption_labels)))
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ax1.set_yticklabels(adoption_labels, fontsize=9.5)
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ax1.set_xlim(0, 105)
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ax1.set_xticks(range(0, 110, 10))
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ax1.tick_params(axis="x", labelsize=8)
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ax1.invert_yaxis()
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for bar, val in zip(bars_a, adoption_values):
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ax1.text(val + 1.5, bar.get_y() + bar.get_height() / 2,
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f"{val}%", va="center", fontsize=10,
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fontweight="bold", color=GRAY_DARK)
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ax1.set_title("AI Coding Tool Adoption Rates",
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fontsize=14, fontweight="bold", pad=10)
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ax1.grid(True, alpha=0.3, axis="x")
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ax1.spines["top"].set_visible(False)
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ax1.spines["right"].set_visible(False)
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# =========================================================
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# Panel B: Code Acceptance Rates
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# =========================================================
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acceptance_labels = [
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"~30% acceptance rate\n(GitHub Copilot)",
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"88% code retention rate\n(GitHub Copilot)",
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"22% of merged code is\nAI-authored (DX DevCycle)",
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"71% do NOT merge AI code\nwithout manual review",
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]
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acceptance_values = [30, 88, 22, 71]
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acceptance_colors = [
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WARNING_ZONE, # 30% acceptance — warning
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NORMAL_ZONE, # 88% retention — good
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AGENT_GROWTH, # 22% AI-authored — neutral
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GRAY_DARK, # 71% manual review — caution signal
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]
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bars_b = ax2.barh(
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range(len(acceptance_labels)),
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acceptance_values,
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color=acceptance_colors,
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edgecolor="white",
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height=0.55,
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)
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ax2.set_yticks(range(len(acceptance_labels)))
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ax2.set_yticklabels(acceptance_labels, fontsize=9.5)
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ax2.set_xlim(0, 105)
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ax2.set_xticks(range(0, 110, 10))
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ax2.tick_params(axis="x", labelsize=8)
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ax2.invert_yaxis()
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for bar, val in zip(bars_b, acceptance_values):
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ax2.text(val + 1.5, bar.get_y() + bar.get_height() / 2,
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f"{val}%", va="center", fontsize=10,
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fontweight="bold", color=GRAY_DARK)
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ax2.set_title("Code Acceptance Rates",
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fontsize=14, fontweight="bold", pad=10)
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ax2.grid(True, alpha=0.3, axis="x")
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ax2.spines["top"].set_visible(False)
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ax2.spines["right"].set_visible(False)
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# Annotation: adoption vs acceptance gap
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ax2.annotate(
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"HUGE GAP:\nHigh adoption,\nlow acceptance",
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xy=(30, 1.8), xytext=(58, 0.8),
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arrowprops=dict(arrowstyle="->", color=BUBBLE_ZONE, lw=2),
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fontsize=10, fontweight="bold", color=BUBBLE_ZONE,
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ha="center",
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bbox=dict(boxstyle="round,pad=0.3", facecolor=GRAY_LIGHT,
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edgecolor=BUBBLE_ZONE, linewidth=1.2),
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)
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# =========================================================
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# Panel C: Code Quality in Production
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# =========================================================
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quality_labels = [
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"29.1% Python AI code has\nsecurity weaknesses",
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"24.2% JavaScript AI code has\nsecurity weaknesses",
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"48% AI-generated code has\npotential vulnerabilities",
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"1.7x more issues in\nAI-coauthored PRs (CodeRabbit)",
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"7.2% drop in delivery\nstability (Google DORA)",
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]
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quality_values = [29.1, 24.2, 48, 1.7, 7.2]
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# All bars use BUBBLE_ZONE to signal danger
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quality_colors = [BUBBLE_ZONE] * len(quality_labels)
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bars_c = ax3.barh(
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range(len(quality_labels)),
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quality_values,
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color=quality_colors,
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edgecolor="white",
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height=0.45,
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)
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ax3.set_yticks(range(len(quality_labels)))
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ax3.set_yticklabels(quality_labels, fontsize=10)
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# X-axis scaled to the max value
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ax3.set_xlim(0, max(quality_values) * 1.25)
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ax3.set_xticks([0, 10, 20, 30, 40, 50])
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ax3.tick_params(axis="x", labelsize=9)
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ax3.invert_yaxis()
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for bar, val in zip(bars_c, quality_values):
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label = f"{val}x" if val < 5 and val != int(val) else f"{val}"
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ax3.text(val + 1, bar.get_y() + bar.get_height() / 2,
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label, va="center", fontsize=11,
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fontweight="bold", color="#c0392b")
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ax3.set_title("Code Quality Concerns in Production",
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fontsize=14, fontweight="bold", pad=10,
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color=BUBBLE_ZONE)
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ax3.grid(True, alpha=0.3, axis="x")
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ax3.spines["top"].set_visible(False)
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ax3.spines["right"].set_visible(False)
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# =========================================================
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# Figure-level title and disclaimer
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# =========================================================
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fig.suptitle(
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"Real-World Developer AI: Adoption vs. Code Quality",
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fontsize=18, fontweight="bold", y=0.96,
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color=GRAY_DARK,
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)
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fig.text(
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0.5, 0.925,
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"Benchmarks measure lab tasks, not production shipping",
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ha="center", fontsize=13, style="italic",
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color=GRAY_DARK, alpha=0.8,
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)
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# Prominent disclaimer banner
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fig.text(
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0.5, 0.015,
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"⚠ Benchmarks measure controlled lab tasks, NOT production shipping",
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ha="center", fontsize=12, fontweight="bold",
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color=BUBBLE_ZONE,
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bbox=dict(
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boxstyle="round,pad=0.5",
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facecolor=GRAY_LIGHT,
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edgecolor=BUBBLE_ZONE,
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linewidth=2,
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),
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)
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# =========================================================
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# Save
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# =========================================================
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out_path = "output/charts/12_developer_ai_reality.png"
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fig.savefig(
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out_path, dpi=EXPORT_DPI,
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facecolor=fig.get_facecolor(),
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edgecolor="none",
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)
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plt.close(fig)
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return out_path
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def main():
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path = plot_developer_reality()
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print(f"Chart saved: {path}")
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if __name__ == "__main__":
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main()
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